Efficient facial landmark localization based on binarized neural networks

Facial landmark localization is a significant yet challenging computer vision task, whose accuracy has been remarkably improved due to the successful application of deep Convolutional Neural Networks (CNNs). However, CNNs require huge storage and computation overhead, thus impeding their deployment on computationally limited platforms. In this paper, to the best of our knowledge, it is the first time that an efficient facial landmark localization is implemented via binarized CNNs. We introduce a new network architecture to calculate the binarized models, referred to as Amplitude Convolutional Networks (ACNs), based on the proposed asynchronous back propagation algorithm. We can efficiently recover the full-precision filters only using a single factor in an end-to-end manner, and the efficiency of CNNs for facial landmark localization is further improved by the extremely compressed 1-bit ACNs. Our ACNs reduce the storage space of convolutional filters by a factor of 32 compared with the full-precision models on dataset LFW+Webface, CelebA, BioID and 300W, while achieving a comparable performance to the full-precision facial landmark localization algorithms.